As a result of anthropogenic impact, significant changes occur in the morphometric, hydrological, chemical and other characteristics of water bodies, which in turn leads to changes in the structure, productivity and condition of aquatic ecosystems. Pollution of water bodies is the result of anthropogenic activities that lead to negative consequences - deterioration of water quality, threat to water bodies, deterioration of life and health of people. In order to monitor the water ecosystems of Akmola region, the research team took water samples from water bodies (Nura, Ishim Rivers of the Republic of Kazakhstan, Akmola region and Taldykol, Maybalyk and Zhaltyrkol Lakes), collected them in a database and conducted research.We constructed a mathematical model, after which the results were compared with full-scale data.According to water samples, the level of pollution of water sources was evaluated, the obtained data was entered into the database of the water ecosystem of Akmola region, and the collected data was predicted using the ARIMA (autoregressive integrated moving average) model and the R programming language.

The internet of things (IoT) encompasses various devices for monitoring, data collection, tracking people and assets, and interacting with other gadgets without human intervention. Implementing a system for predicting the development and assessing the criticality of detected attacks is essential for ensuring security in IoT interactions. This work analyses existing methods for detecting attacks, including machine learning, deep learning, and ensemble methods, and explores the federated learning (FL) method. The aim is to study FL to enhance security, develop a methodology for predicting the development of attacks, and assess their criticality in real-time. FL enables devices and the aggregation server to jointly train a common global model while keeping the original data locally on each client. We demonstrate the performance of the proposed methodology against structured query language (SQL) injection and brute force attacks using the CICIOT2023 dataset. We used accuracy and F1 score metrics to evaluate the effectiveness of our proposed methodology. As a result, the accuracy in predicting SQL injection reached 100%, and for brute force attacks, it reached 98.25%. The high rates of experimental results clearly show that the proposed FL-based attack prediction methodology can be used to ensure security in IoT interactions.

Nowadays, the branch of mobile robotics rapidly develops. The environment of projecting in the field of mobile robotics changes from specialized to a specific and universal platform. This article is devoted to researching of development prediction and development requirements to a mobile robotic system. The problems of development and distribution of mobile robotic systems in the areas of risk to human health and life, the stages of designing of mobile robotic system were considered. Hardware-based serial mini-tractor "Belarus-132N" considers in the article. Systems and units that will be included in the complex as components are offered. The choice of mini-tractor with a manual transmission is justified. One of the main projecting objective functions is to ensure the affordability of the product that will be achieved at the expense of the developed hardware and software.

This paper presents an architecture design for a patient monitoring system integrated with Internet of Things (IoT) technology to detect and quantify patient stress levels. Research in remote patient prediction systems is considered one of the most important areas at present. This technology offers the potential to improve stress assessment, provide interventional treatment, and provide personalized stress management techniques. A Raspberry Pi microcontroller was used as a key controller. The unit is equipped with electroencephalography sensors, electrocardiogram sensors, glucose sensors, and electromyography sensors to record physiological signals indicative of stress, such as cardiac activity and human brain activity, a method for monitoring blood glucose levels in diabetic patients and measuring electrical activity. Muscles are collected from these four sensors and transmit information via communication channels (Wi-Fi, USB). The information obtained is transferred to a storage database, where patient data is securely stored. In the storage database, interaction between the patient and the doctor occurs via a 4G communication channel. Data is transmitted via a 4G communication channel from the storage database to the doctor’s personal computer. From the doctor’s personal computer, data is transferred to the doctor’s control panel, and from there the data is transferred to a web server, where all data is processed and the patient is monitored. In the course of research, it was found that the proposed device has 95% reliability in measuring cardiac activity and human brain activity, a method for monitoring blood glucose levels in patients with diabetes and measuring the electrical activity of muscles.

The purpose of the study is to solve an extreme mathematical problem—semantic analysis of natural language, which can be used in various fields, including marketing research, online translators, and search engines. When training the neural network, data training methods based on the latent Dirichlet allocation model and vector representation of words were used. This study presents the development of a neurocomputer system used for the purpose of semantic analysis of the text in the Kazakh language, based on machine learning and the use of the latent Dirichlet allocation model. In the course of the study, the stages of system development were considered, regarding the text recognition algorithm. The Python programming language was used as a tool using libraries that greatly simplify the process of creating neural networks, including the Keras library. An experiment was conducted with the involvement of experts to test the effectiveness of the system, the results of which confirmed the reliability of the data provided by the system. The papers of modern computer linguists dealing with the problems of natural language processing using various technologies and methods are considered.